I always explain that Workflows should be thought of as a fancier version of Kubernetes Jobs. Some of the killer features, IMO, are with artifact management and

I always explain that Workflows should be thought of as a fancier version of Kubernetes Jobs. Some of the killer features, IMO, are with artifact management and control flow (e.g. loops, conditionals, parallelism). So far, our most common use case has been in the machine learning space. One of the biggest problems it can solve is the ability to leverage a Kubernetes cluster as an auto-scaling compute grid for all forms batch processing. We've been working closely with ML teams at various companies, both large and small, and many of our features were implemented to address the ML use cases (e.g. DAG support, loops/step expansion, step aggregation). With that said, Argo got its start trying to address the CI use case, so we also feel like we handle that one pretty well. The workflow controller has always been intended to be a lego block to enable higher level applications to be built on top of it.